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@ARTICLE{Thei:1023025,
      author       = {Theiß, Marie and Steier, Angelina and Rascher, Uwe and
                      Müller-Linow, Mark},
      title        = {{C}ompleting the picture of field-grown cereal crops: a new
                      method for detailed leaf surface models in wheat},
      journal      = {Plant methods},
      volume       = {20},
      number       = {1},
      issn         = {1746-4811},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {FZJ-2024-01608},
      pages        = {21},
      year         = {2024},
      abstract     = {Background: The leaf angle distribution (LAD) is an
                      important structural parameter of agricultural crops that
                      influences light interception, radiation fluxes and
                      consequently plant performance. Therefore, LAD and its
                      parametrized form, the Beta distribution, is used in many
                      photosynthesis models. However, in field cultivations, these
                      parameters are difficult to assess and cereal crops in
                      particular pose challenges since their leaves are thin,
                      flexible, and often bent and twisted around their own axis.
                      To our knowledge, there is only a very limited set of
                      methods currently available to calculate LADs of field-grown
                      cereal crops that explicitly takes these special
                      morphological properties into account.Results: In this
                      study, a new processing pipeline is introduced that allows
                      for the generation of realistic leaf surface models and the
                      analysis of LADs of field-grown cereal crops from 3D point
                      clouds. The data acquisition is based on a convenient stereo
                      imaging setup. The approach was validated with different
                      artificial targets and results on the accuracy of the 3D
                      reconstruction, leaf surface modeling and calculated LAD are
                      given. The mean error of the 3D reconstruction was below 1mm
                      for an inclination angle range between 0° and 75° and the
                      leaf surface could be quantified with an average accuracy of
                      $90\%.$ The concordance correlation coefficient (CCC) of
                      $99.6\%$ (p-value = 1.5* $〖10〗^(-29))$ indicated a high
                      correlation between the reconstructed inclination angle and
                      the identity line. The LADs for bent leaves were
                      reconstructed with a mean error of 0.21° and a standard
                      deviation of 1.55°. As an additional parameter, the
                      insertion angle was reconstructed for the artificial leaf
                      model with an average error < 5°. Finally, the method was
                      tested with images of field-grown cereal crops and Beta
                      functions were approximated from the calculated LADs. The
                      mean CCC between reconstructed LAD and calculated Beta
                      function was 0.66. According to Cohen, this indicates a high
                      correlation.Conclusion:This study shows that our image
                      processing pipeline can reconstruct the complex leaf shape
                      of cereal crops from stereo images. The high accuracy of the
                      approach was demonstrated with several validation
                      experiments including artificial leaf targets. The derived
                      leaf models were used to calculate LADs for artificial
                      leaves and naturally grown cereal crops. This helps to
                      better understand the influence of the canopy structure on
                      light absorption and plant performance and allows for a more
                      precise parametrization of photosynthesis models via the
                      derived Beta distributions.},
      cin          = {IBG-2},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217) / DPPN - Deutsches Pflanzen
                      Phänotypisierungsnetzwerk (BMBF-031A053A) / DFG project
                      390732324 - EXC 2070: PhenoRob - Robotik und
                      Phänotypisierung für Nachhaltige Nutzpflanzenproduktion
                      (390732324)},
      pid          = {G:(DE-HGF)POF4-2171 / G:(DE-Juel1)BMBF-031A053A /
                      G:(GEPRIS)390732324},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38310295},
      UT           = {WOS:001156727200002},
      doi          = {10.1186/s13007-023-01130-x},
      url          = {https://juser.fz-juelich.de/record/1023025},
}